class_docstr: Distributed Logistic Regression for Binary classification.
  
  
  Parameters
  ----------
  fit_intercept: boolean (default = True)
     If True, the model tries to correct for the global mean of y.
     If False, the model expects that you have centered the data.
  solver : 'admm'
      Solver to use. Only admm is supported currently.
  penalty : {'l1', 'l2', 'elastic_net'} (default = 'l2')
      Regularization technique for the solver.
  C: float (default = 1.0)
     Inverse of regularization strength; must be a positive float.
  max_iter: int (default = 100)
      Maximum number of iterations taken for the solvers to converge.
  verbose : int or boolean (default=False)
      Sets logging level. It must be one of `cuml.common.logger.level_*`.
      See :ref:`verbosity-levels` for more info.
  
  Attributes
  ----------
  coef_: device array (n_features, 1)
      The estimated coefficients for the logistic regression model.
  intercept_: device array (1,)
      The independent term. If `fit_intercept` is False, will be 0.
  solver: string
      Algorithm to use in the optimization process. Currently only `admm` is
      supported.
  
  Notes
  ------
  
  This estimator is a wrapper class around Dask-GLM's
  Logistic Regression estimator. Several methods in this wrapper class
  duplicate code from Dask-GLM to create a user-friendly namespace.
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code: def decision_function(self, X):
      X = self._input_to_dask_cupy_array(X)
      X_ = self._maybe_add_intercept(X)
      return np.dot(X_, self._coef)
  
file_path: cuml/python/cuml/dask/extended/linear_model/logistic_regression.py
imports: from dask.utils import is_dataframe_like, is_series_like, is_arraylike
  from cuml.internals.safe_imports import cpu_only_import
  from cuml.dask.common.base import BaseEstimator
  from cuml.common import with_cupy_rmm
  from cuml.internals.import_utils import has_daskglm
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 5 for calling the LogisticRegression obj's underlying member method code with example usage: obj.decision_function(X) and returns: np